Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data.

Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in functionality and accuracy with respect to current deconvolution methods. Using ConDecon, we discover the implication of neurodegenerative microglial inflammatory pathways in the mesenchymal transformation of ependymoma, recapitulate spatial patterns of cell differentiation during zebrafish embryogenesis, and make temporal inferences from bulk ATAC-seq data. Overall, ConDecon significantly enhances our understanding of dynamic cellular processes within bulk tissue samples.

Pro-inflammatory cytokines mediate the epithelial-to-mesenchymal-like transition of pediatric posterior fossa ependymoma.

Pediatric ependymoma is a devastating brain cancer marked by its relapsing pattern and lack of effective chemotherapies. This shortage of treatments is due to limited knowledge about ependymoma tumorigenic mechanisms. By means of single-nucleus chromatin accessibility and gene expression profiling of posterior fossa primary tumors and distal metastases, we reveal key transcription factors and enhancers associated with the differentiation of ependymoma tumor cells into tumor-derived cell lineages and their transition into a mesenchymal-like state. We identify NFκB, AP-1, and MYC as mediators of this transition, and show that the gene expression profiles of tumor cells and infiltrating microglia are consistent with abundant pro-inflammatory signaling between these populations. In line with these results, both TGF-ß1 and TNF-α induce the expression of mesenchymal genes on a patient-derived cell model, and TGF-ß1 leads to an invasive phenotype. Altogether, these data suggest that tumor gliosis induced by inflammatory cytokines and oxidative stress underlies the mesenchymal phenotype of posterior fossa ependymoma.

Single-cell antigen-specific landscape of CAR T infusion product identifies determinants of CD19-positive relapse in patients with ALL.

A notable number of acute lymphoblastic leukemia (ALL) patients develop CD19-positive relapse within 1 year after receiving chimeric antigen receptor (CAR) T cell therapy. It remains unclear if the long-term response is associated with the characteristics of CAR T cells in infusion products, hindering the identification of biomarkers to predict therapeutic outcomes. Here, we present 101,326 single-cell transcriptomes and surface protein landscape from the infusion products of 12 ALL patients. We observed substantial heterogeneity in the antigen-specific activation states, among which a deficiency of T helper 2 function was associated with CD19-positive relapse compared with durable responders (remission, >54 months). Proteomic data revealed that the frequency of early memory T cells, rather than activation or coinhibitory signatures, could distinguish the relapse. These findings were corroborated by independent functional profiling of 49 patients, and an integrative model was developed to predict the response. Our data unveil the molecular mechanisms that may inform strategies to boost specific T cell function to maintain long-term remission.

A roadmap for the Human Developmental Cell Atlas.

The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development.

Single-cell multiomics dissection of basal and antigen-specific activation states of CD19-targeted CAR T cells.

BACKGROUND: Autologous T cells engineered to express a chimeric antigen receptor (CAR) specific for CD19 molecule have transformed the therapeutic landscape in patients with highly refractory leukemia and lymphoma, and the use of donor-generated allogeneic CAR T is paving the way for further breakthroughs in the treatment of cancer. However, it remains unknown how the intrinsic heterogeneities of these engineered cells mediate therapeutic efficacy and whether allogeneic products match the effectiveness of autologous therapies. METHODS: Using single-cell mRNA sequencing in conjunction with CITE-seq, we performed multiomics characterization of CAR T cells generated from healthy donor and patients with acute lymphoblastic leukemia. CAR T cells used in this study were manufactured at the University of Pennsylvania through lentiviral transduction with a CD19-4-1BB-CD3ζ construct. Besides the baseline condition, we engineered NIH-3T3 cells with human CD19 or mesothelin expression to conduct ex vivo antigen-specific or non-antigen stimulation of CAR T cells through 6-hour coculture at a 1:1 ratio. RESULTS: We delineated the global cellular and molecular CAR T landscape and identified that transcriptional CAR tonic signaling was regulated by a mixture of early activation, exhaustion signatures, and cytotoxic activities. On CD19 stimulation, we illuminated the disparities of CAR T cells derived from different origins and found that donor CAR T had more pronounced activation level in correlation with the upregulation of major histocompatibility complex class II genes compared with patient CAR T cells. This finding was independently validated in additional datasets from literature. Furthermore, GM-CSF(CSF2) expression was found to be associated with functional gene productions, but it induced little impact on the CAR T activation. CONCLUSIONS: Through integrated multiomics profiling and unbiased canonical pathway analyses, our results unveil heterogeneities in the transcriptional, phenotypic, functional, and metabolic profiles of donor and patient CAR T cells, providing mechanistic basis for ameliorating clinical outcomes and developing next-generation 'off- the-shelf' allogeneic products.

Identification of relevant genetic alterations in cancer using topological data analysis.

Large-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma. We show that ADAMTS12-/- mice have a five-fold increase in the susceptibility to develop lung tumors, confirming the role of ADAMTS12 as a tumor suppressor gene. Our results demonstrate that data integration through topological techniques can increase our ability to identify previously unreported cancer-related alterations.

Topological methods for genomics: present and future directions.

Topological methods are emerging as a new set of tools for the analysis of large genomic datasets. They are mathematically grounded methods that extract information from the geometric structure of data. In the last few years, applications to evolutionary biology, cancer genomics, and the analysis of complex diseases have uncovered significant biological results, highlighting their utility for fulfilling some of the current analytic needs of genomics. In this review, the state of the art in the application of topological methods to genomics is summarized, and some of the present limitations and possible future developments are reviewed.

Spatiotemporal genomic architecture informs precision oncology in glioblastoma.

Precision medicine in cancer proposes that genomic characterization of tumors can inform personalized targeted therapies. However, this proposition is complicated by spatial and temporal heterogeneity. Here we study genomic and expression profiles across 127 multisector or longitudinal specimens from 52 individuals with glioblastoma (GBM). Using bulk and single-cell data, we find that samples from the same tumor mass share genomic and expression signatures, whereas geographically separated, multifocal tumors and/or long-term recurrent tumors are seeded from different clones. Chemical screening of patient-derived glioma cells (PDCs) shows that therapeutic response is associated with genetic similarity, and multifocal tumors that are enriched with PIK3CA mutations have a heterogeneous drug-response pattern. We show that targeting truncal events is more efficacious than targeting private events in reducing the tumor burden. In summary, this work demonstrates that evolutionary inference from integrated genomic analysis in multisector biopsies can inform targeted therapeutic interventions for patients with GBM.

Evolutionary scalpels for dissecting tumor ecosystems.

Amidst the growing literature on cancer genomics and intratumor heterogeneity, essential principles in evolutionary biology recur time and time again. Here we use these principles to guide the reader through major advances in cancer research, highlighting issues of "hit hard, hit early" treatment strategies, drug resistance, and metastasis. We distinguish between two frameworks for understanding heterogeneous tumors, both of which can inform treatment strategies: (1) The tumor as diverse ecosystem, a Darwinian population of sometimes-competing, sometimes-cooperating cells; (2) The tumor as tightly integrated, self-regulating organ, which may hijack developmental signals to restore functional heterogeneity after treatment. While the first framework dominates literature on cancer evolution, the second framework enjoys support as well. Throughout this review, we illustrate how mathematical models inform understanding of tumor progression and treatment outcomes. Connecting models to genomic data faces computational and technical hurdles, but high-throughput single-cell technologies show promise to clear these hurdles. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.